Mining Vehicle Location Tracking with Real-Time Arrival Timelines
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Solution Overview
Problem
Current mine mapping equipment lacks a comprehensive view of vehicle locations, endpoint statuses, and expected arrival times, failing to provide real-time operational guidance for optimizing vehicle operations and reducing inefficiencies in mining environments.
Innovation Solution
A system and method that utilize vehicle location information to generate and display timelines and maps, integrating real-time location, direction, and speed data to calculate and display estimated travel times, and show vehicle locations on timelines and overhead maps, while also managing incident information and optimizing vehicle routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If comprehensive real-time monitoring of vehicle locations and endpoint statuses is implemented, then operational efficiency is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple monitoring functions (vehicle location tracking, endpoint status monitoring, timeline generation, and operational guidance) into a single integrated system. The computer system merges GPS data, vehicle telemetry, and endpoint status information into unified visual displays, reducing the need for separate monitoring systems and simplifying operational workflows while maintaining comprehensive surveillance.
Solution Approach 2:
The monitoring system performs multiple functions simultaneously: it tracks vehicle locations, calculates estimated arrival times, displays endpoint statuses, generates timelines, and provides operational guidance. This multi-functional approach consolidates what would otherwise require separate systems into one universal platform, improving efficiency without proportionally increasing complexity.
2Ease of operation
If real-time location tracking and timeline display are implemented for all vehicles, then vehicle operation management is improved, but information processing requirements increase
Solution Approach 1:
The system segments information presentation by creating distinct visual displays for different aspects of vehicle management: overhead maps show current locations, timelines display scheduled activities and estimated arrival times, and separate indicators show endpoint statuses. This segmentation organizes large volumes of data into manageable, easily interpretable segments that reduce cognitive load on operators.
Solution Approach 2:
The patent transforms temporal data into spatial visualizations by displaying vehicle progress along timeline graphics that represent time dimensions. Instead of presenting raw time-stamped data, the system projects estimated arrival times and vehicle positions onto visual timelines, adding a spatial dimension to temporal information that makes processing and interpretation more efficient.
3Loss of time
If estimated travel time calculation and display are implemented, then delivery scheduling is improved, but computational requirements increase
Solution Approach 1:
The system calculates estimated arrival times in advance based on current vehicle positions, speeds, and predefined route information. By performing these computations proactively rather than reactively, the system prepares scheduling information before it is needed for decision-making, reducing real-time computational demands while improving scheduling efficiency.
Solution Approach 2:
The system continuously updates estimated arrival times based on real-time vehicle telemetry data and compares these against scheduled delivery times. This feedback mechanism allows the system to adjust predictions dynamically without requiring complex recalculation from scratch, using incremental updates that reduce computational requirements while maintaining accuracy.
Data Source
AI summary
A system of computers, wireless networks, and vehicle-based location sensors allows real time display of equipment location, utilization, and expected arrival times for mobile vehicles. Display of location by load status and expected arrival time allows monitoring of not just vehicle location but the impact on queue times at loading and unloading endpoints allowing for equipment reallocation. Overhead map views of actual location including hazard locations and queries for vehicle and operator status are also supported.